These large differences
show that income changes affect low- and high-income families in
significantly different ways. Low-income families tend to
increase fat intake and % fat more than high-income families. The
increase is usually attributed to consumption of high-fat foods.
These differences demonstrate two perspectives: as income
improves, diet quality increases, which helps to eliminate
undernutrition; and at the same time, as income increases, there
are some negative effects in that rapid increases in fat intake
are associated with increased likelihood of obesity and related
chronic diseases. Programmes to alleviate poverty or develop the
economy should take into account the issues of dietary deficit
and excess.

TABLE 8. Signs of the coefficients from
the two regimens

Low- income regimen

High-income regimen

Fat

Calories

% fat

+

education smoking moderate
activity

alcohol use income

male smoking alcohol use northern
moderate activity heavy activity

income

education smoking urban

age moderate activity income

-

urban heavy activity family size
northern

age male

age education

family size urban

male alcohol use family size
northern heavy activity

TABLE 9. The simulated effects of
changes in selected variables on nutrient intake

Change

Fat

Calories

% fat

One
regimen

Low-
income regimen

High
- income regimen

One
regimen

Low-
income regimen

High
- income regimen

One
regimen

Low-
income regimen

High
income regimen

Income

+ Y 1.000

0.800

3.40

-0.200

-5.1

2.8

-0.3

0.300

1.000

- 0.100

Age

+1 year

-0.071

-0.110

-0.065

-5.9

-6.9

0.3

0.022

0.015

-0.007

Gender

female to
male

-6.461

- 7.042

-1.779

260.1

259.7

270.8

-3.963

-4.039

- 3.138

Alcohol use

2.038

2.724

-1.370

112.6

127.7

24.2

-0.044

-0.008

-0.141

Urban

rural to
urban

1.020

-0.484

3.714

-143.2

-127.9

-243.6

2.001

1.338

4.14 1

Physical
activity

light to
moderate

4.295

5.002

0.834

83.2

92.4

53.7

0.449

0.583

-0.476

light to
heavy

-6.392

-5.671

1.847

291.6

301.4

262.2

-4.250

-4.053

-1.972

Other factors affecting
nutrient intake

The differences in
nutrient consumption behaviour between the two groups are also
present for age, gender, alcohol use, urban or rural residence,
and physical activity effects. For example, when age increases
one year, fat intake decreases significantly 0.11 g for
low-income families yet decreases only insignificantly 0.065 g
for high-income families. Caloric intake decreases significantly
6.9 kcal for low-income families yet increases insignificantly
0.3 kcal for highincome families. The % fat from energy increases
0.015 percentage point and decreases 0.007 percentage point for
the two respectively. Both are insignificant. When rural
residents move to urban areas, the fat intake of low-income
families decreases 0.48 g, while that of high-income families
increases 3.7 g, although both are insignificant. Caloric intake
significantly decreases for both types of families; the decrease
in high-income families is twice that in low-income families. In
high-income families, the % fat increase is almost four times
that of low-income families. This shows that the diet of
low-income families may be impaired when they move to urban
areas, whereas the diet of high-income families could be
improved.

Similarly, physical
activity is associated differently with fat and caloric intakes
according to income. Moderate physical activity considerably
increases the absolute fat intake of the low-income regimen.
Heavy physical activity considerably decreases the fat intake of
the low-income regimen but increases that of the high-income
regimen. There is no clear interpretation of these differences.

Although the effects of
some of the variables on certain nutrients are not statistically
significant, they are theoretically or practically associated
with food or nutrient consumption and may potentially help to
interpret consumption behaviour. Thus, they are still included in
the equation and the discussion.

Other considerations

The results suggest that
when income and other factors vary, the propensity to increase or
decrease some nutrients is substantially different for low-income
and high-income families. The effects on the former families are
stronger.

The estimates of
coefficients a and ß are similar for some
variables in terms of their magnitude and direction. It is
possible that the effects of some components of a and ß are equal. Therefore, it is worth
while to perform some additional tests to verify whether Hi: ai= ßi. Computed
statistics are based on the t statistic [20]. The results (table
10) indicate no significant difference among the influence of
education, family size, smoking status, and northern residence on
fat intake in low- and high-income households.

TABLE 10. t test of equality of
coefficients of two regimens

Explanatory variable

Fat

Calories

% fat

Education

0.92

6.37*

1.52

Smoking

1.77

4.03*

3.61 *

Family size

1.02

0.58

1.10

Northern residence

1.64

4.83

1.28

Values are t
statistics.
Significant at 1% level.

It may be argued that a
number of changes may have occurred concurrently with growth in
income. For example, education level improved, and changes in
agricultural policy resulted in changes in the nature of the food
supply by expanding possible choices or by reducing market prices
because of a more stable supply. Indeed, the market price
increased considerably in past years, which affects the
lower-income group more than the high-income group. Compared with
full income growth and its effect on diet and all other aspects
in China over the past 10 years, most of the other changes
occurring during the time would scarcely bias our results.

There is substantial structural difference in
consumption behaviour between groups whose household income is
below Y7,000 per year and those whose income is greater. The
results indicate that income increases operate differently for
very high-income families than for other families. Among
high-income families in the multivariate analysis, income
increases are associated with decreased fat and calorie intake,
whereas the opposite occurs among lower-income families. In
particular, a Y1,000 increase would result in a 3.4-g increase in
fat intake in lower-income families. If this same relationship
were examined with one model, then a Y1,000 increase in income
would result in a 0.8-g increase. It is clear that a very
different sense of the magnitude of the income and fat
relationship would be derived from these two models. The results
also show very different effects of area of residence, physical
activity patterns, and several other factors on diet.

At present in China the focus is on the
nutrition transition. The government organized the National
Commission for Food Reform and Development to address many of the
problems noted in this paper.

This represents a path-breaking effort for a
low-income country to try to address problems of under-and
over-nutrition concurrently. The size of relationships such as
income and fat, particularly as it relates to income and price
increases, is of particular importance to this and a second
commission in China. Ignoring this relationship can mask
important income and dietary intake relationships and lead to
misleading conclusions.

This study also fits into a larger set of
changes that are affecting many lower-income countries as they
develop. A large transition in diet is occurring in these
countries, and its implications for each income group should be
understood [22].

11. Alderman H. New research on poverty and
malnutrition: what are the implications for research and policy?
In: Lipton M, Van der Gaag J. eds. Including the poor.
Washington, DC: World Bank, 1992:115-31.

The world imbalance of dietary essential
amino acids was studied using the latest available protein-supply
data (1987-89) and the revised 1989 FAO/WHO protein scoring
pattern in comparison with the 1973 FAO/WHO pattern, the 1985
FAO/WHO/UNU pattern, and a pattern proposed by Young et al. in
1989. The results obtained using the 1989 FAO/WHO scoring pattern
indicate that the first limiting amino acid for developed
countries is usually tryptophan, and that for developing
countries is mainly lysine. Similar findings resulted with the
Young pattern, but results using the 1973 and 1985 patterns
differed substantially. On the basis of the 1989 FAO/WHO pattern,
lysine was found to be the first limiting amino acid in the
dietary protein supplies of 121 of the 164 countries studied
worldwide; it is estimated that the total lysine deficiency in
these 121 countries, the amount that would be needed to bring it
to the level of the second limiting amino acid, was 1.15 million
metric tons per year for 1987-89. In addition, same global
correlations of protein and amino acid supplies with gross
domestic product were recalculated in US dollars at 1985 prices.

Introduction

In an earlier paper [1], I reported on the
imbalance between supplies of and requirements for essential
amino acids (EAA) by country, region, and economic system and for
the world, with averages for four three-year periods, 1972-74,
1975-77, 1979-81, and 198486, calculated in accordance with the
1973 FAO/WHO scoring pattern [2], the 1985 FAO/ WHO/UNU scoring
pattern [3], and a scoring pattern proposed in 1989 by Young et
al. [4, 5].

In 1989, on the basis of new evidence, FAO/WHO
published a revised EAA scoring pattern [6] to correct the 1985
pattern. The 1989 report recommended that the amino acid
composition of human milk should continue to be the basis of the
scoring pattern to evaluate protein quality in foods for infants
under 1 year of age, but that the amino acid scoring pattern
proposed in 1985 by FAO/WHO/ UNU for children of preschool age
(2-5 years) should be used to evaluate dietary protein quality
for all age groups above infancy (table 1). The 1989 FAO/WHO
pattern has EAA requirements similar to those of the Young
pattern but with a higher requirement for lysine, a slightly
higher requirement for tryptophan, and a slightly lower one for
leucine.

In addition, the statistical data for average
annual protein supply and gross domestic product (GDP) by country
are now available for 1987-89.

In view of the worldwide significance of those
developments, the change in the calculated nutritional imbalance
of EAA is here re-evaluated using the 1989 FAO/WHO scoring
pattern in comparison with the previous patterns to revise the
conclusions of my earlier paper [1]. (Part of this study has been
presented elsewhere [7].) Furthermore, the correlations of GDP
with total protein supplies and animal protein ratios (APR) are
recalculated in US dollars at 1985 prices to update similar data
in the previous paper.